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The world is awash in data like never before. From a person’s morning Uber ride and favorite coffee spot, to the emails sent from their office—all these activities create massive amounts of data, but also behavioral and investment insights.
Warren Buffett’s investment style exemplifies the fundamental approach: “Which companies offer the best returns?”
On the other hand, hedge fund manager James Simons of Renaissance Technologies is a notable example of the quantitative approach: “What is the best way to predict returns?”
Both techniques have one thing in common—they seek excess return from the marketplace, or what is known as “Alpha”.
The infographic comes from from GoldSpot Discoveries, and it outlines quantamental investing as the blending of these two styles, human insight with computer power.
Despite both methods seeking excess returns in the market, there are some key differences:
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The arrival of advanced sensor technology and computer processing power is creating huge opportunities for capturing the complexity of human activity on a larger scale.
Could these two distinct methods be fused together?
A New Frontier for Data: Combining Man and Machine
On a larger scale, tracking and storing data can reveal economic patterns over long periods of time. For example, satellite images of a mall’s parking lot can determine the mall’s sales volume. In the finance world, software can track sentiment in earnings call transcripts, and detect word patterns of executives.
The applications of sensor technology stretch across various cases, and could improve overall performance in different industries.
Case #1: Sabermetrics
Picking a winning baseball team is a lot like investing: with limited capital, one needs to optimize player selection and performance to beat the competition. That is why the Major League Baseball Association installed StatScan in 30 ballparks for 3 seasons (2015-2017).
These radar and camera systems captured the raw skills of players in ways that were previously available to or only understood by the baseball scouts.
Scouts are the stock pickers of the baseball. They know the ins and outs of a potential major league player, and consider health, family history, body mechanics and even personalities.
Team managers can use a scout’s insight, against the vast amounts of data collected during a baseball season, to uncover the exact metrics to predict the success of the next great home run or strike-out king.
Case #2: Mineral Exploration
Resource companies spend huge amounts of money on exploration to collect data. However, the volume of data generated is too much for one geologist, or even a team to sift through in a reasonable time.
Machine learning in mineral exploration can take in training data to help identify prospective land for a mineral deposit.
Computer Power with a Human Touch
Quantamental investing seeks to understand the depth and the breadth of the investment world. The goal is to produce superior returns in the marketplace by answering two questions.
- What are the best metrics for predicting success?
- Which are the companies performing the best on these metrics?
Quantamental investing harnesses the raw power and scale of data, coupled with human insight — increasing market returns by finding the next great investment.
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